1. Albert Decatur | Dan Runfola | Nick Giner | Rahul Rakshit
Advisors: Prof. Colin Polsky | Prof. Robert Gilmore Pontius, Jr

2. Research Questions
How can we produce a very high resolution land-
cover dataset for a large suburban landscape?
How are lawns of varying extents spatially
distributed across the landscape?
How can we use virtual globes to assess the
accuracy of the dataset?
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3. Overall Project Goals
Create a land-cover data set for 26 towns in NE Massachusetts
Use virtual field work to assess the accuracy of the data
Produce land-cover summaries at various geographies (e.g.
parcels or census blocks)
Develop a tutorial for object-oriented classification of high-
resolution imagery
Streamline data production by developing automated data
processing models
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4. Study Area
Worcester
HOLMES http://sites.google.com/site/heromapmanual/ 4 3

6. Object-oriented classification methodology
HOLMES http://sites.google.com/site/heromapmanual/ 5 5

7. HERO Mapping Project http://sites.google.com/site/heromapmanual/ 5

8. Object-oriented classification methodology
‘05 Aerial Photos
Woburn, MA
HERO Mapping Project http://sites.google.com/site/heromapmanual/ 6
6

9. Aerial
Object-oriented classification methodology
Training
Segmentation Sites Classification
Edge Aggregation Post Manual
Matching and Merging Processing Editing
Final
Thematic Validation
Layers: Product
Impervious
•Impervious Sand
•Water Water
•Wetlands Wetlands
Bare Soil
Coniferous
Deciduous
Fine Green/ Grass
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6

10. Object-oriented classification methodology
HOLMES http://sites.google.com/site/heromapmanual/ 7 7

11. Object-oriented classification methodology
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13. 10
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14. 40
40
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15. 100
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16. 200
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17. HOLMES http://sites.google.com/site/heromapmanual/ 13 12

19. HOLMES http://sites.google.com/site/heromapmanual/ 14

20. HOLMES http://sites.google.com/site/heromapmanual/ 15

21. Out
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22. Out
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23. Out
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24. Out
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25. Out
Impervious
Water
Wetlands
Bare Soil
Coniferous
Deciduous
Fine Green/ Grass
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26. Data Analysis
HOLMES

27. Percentage Land-cover by Town
Data Analysis
Woburn Ipswich
Sand Grass
1% 15%
Bare Soil
26%
Imperviou Deciduou
s s
7% 32%
Wetland
Coniferou
7% Water
s
4%
8%
HOLMES
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28. Percentage Land-cover by Census Block: #44010
All Parcels within Census Block
Data Analysis
Census Block as a Whole
census block parcels selected
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29. Data Analysis Percentage Land-cover by Parcel:
7 Roman Rd: #710309 9 Roman Rd: #710308
Impervious
11%
Impervious
20%
Grass
Coniferous Grass 44%
24% 48%
Coniferous
24%
Deciduous Deciduous
17% 12%
Images courtesy of Google street view
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30. Automation
Streamline data preparation
and processing
Reduce manual steps as well
as likelihood of error
Save time and increase
productivity
HOLMES http://sites.google.com/site/heromapmanual/ 24 22

31. Analysis Model
Data Processing Models
HOLMES http://sites.google.com/site/heromapmanual/ 27 25

32. Percentage of fine green Per Parcel, Burlington in 2005
(For parcels with fine green category)
900
750
Number of Parcels
600
450
300
150
0
0 10 20 30 40 50 60 70 80 90 100
Percentage of fine green
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33. Validation by virtual fieldwork

34. Google Earth and Microsoft Virtual Earth
Better Science : Stratified truly random
sampling that is temporally matching
Very Intuitive
Saves time and money
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35. HOLMES http://sites.google.com/site/heromapmanual/ 31 31

36. HOLMES http://sites.google.com/site/heromapmanual/ 32 32

37. HOLMES http://sites.google.com/site/heromapmanual/ 33 33

38. HOLMES http://sites.google.com/site/heromapmanual/ 34 34

39. HOLMES http://sites.google.com/site/heromapmanual/ 35 35

40. HOLMES http://sites.google.com/site/heromapmanual/ 37 37

41. HOLMES http://sites.google.com/site/heromapmanual/ 38 38

42. HOLMES http://sites.google.com/site/heromapmanual/ 39 39

43. HOLMES http://sites.google.com/site/heromapmanual/ 40 40

44. Virtual Fieldwork Land-cover
Bare Fine Grand
Soil Coniferous Deciduous Green Impervious Water Wetlands Total
Bare
Definiens Land-cover
Soil 34 1 1 1 3 40
Coniferous 37 2 1 40
Deciduous 3 55 2 60
Fine
Green 1 1 57 1 60
Impervious 44 44
Water 40 40
Wetlands 40 40
Grand
Total 38 39 58 59 50 40 40 324
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45. Online Tutorial

46. Google Analytics Visits

47. Acknowledgements
We sincerely thank:
•Prof. Colin Polsky
•Prof. Robert Gilmore Pontius Jr.
•National Science Foundation
•BES (Baltimore) LTER: Jarlath O'Neil-Dunne, Weiqi Zhou, &
Morgan Grove
•MassGIS
•Clark University HERO program
This material is based upon work supported by the National Science Foundation under Grant No. 0709685
Any opinions, findings, & conclusions or recommendations expressed in this material are those of the author(s) & do not necessarily reflect the views of the National Science Foundation.
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